app.py

import streamlit as st
from transformers import T5Tokenizer, T5ForConditionalGeneration
from transformers import pipeline

# Model and tokenizer loading
checkpoint = "./model/google/flan-t5-small"  # Use the smaller "t5-small" model
tokenizer = T5Tokenizer.from_pretrained(checkpoint)
base_model = T5ForConditionalGeneration.from_pretrained(checkpoint)

# LLM pipeline
def llm_pipeline(text):
    # Use the pipeline to generate the summary
    pipe_sum = pipeline(
        'summarization',
        model=base_model,
        tokenizer=tokenizer,
        max_length=500,
        min_length=50
    )

    result = pipe_sum(text)
    summary = result[0]['summary_text']
    return summary

# Streamlit code
st.set_page_config(layout="wide")

def main():
    st.title("Document Summarization App using a Smaller Model")

    # Text input area
    uploaded_text = st.text_area("Paste your document text here:")

    if uploaded_text:
        if st.button("Summarize"):
            summary = llm_pipeline(uploaded_text)

            # Display the summary
            st.info("Summarization Complete")
            st.success(summary)

if __name__ == "__main__":
    main()